Back

Implementing the precautionary approach into fisheries management: Making the case for probability-based harvest control rules

Mildenberger, T.; Berg, C. W.; Kokkalis, A.; Hordyk, A. R.; Wetzel, C.; Jacobsen, N. S.; Punt, A. E.; Nielsen, J. R.

2020-11-08 ecology
10.1101/2020.11.06.369785 bioRxiv
Show abstract

The precautionary approach to fisheries management advocates for risk-averse management strategies that include biological reference points as well as decision rules and account for scientific uncertainty. In this regard, two approaches have been recommended: (i) harvest control rules (HCRs) with threshold reference points to safeguard against low stock biomass, and (ii) the P* method, a probability-based HCR that reduces the catch limit as a function of scientific uncertainty (i.e. process, model, and observation uncertainty). This study compares the effectiveness of these precautionary approaches in recovering over-exploited fish stocks with various life-history traits and under a wide range of levels of scientific uncertainty. We use management strategy evaluation based on a stochastic, age-based operating model with quarterly time steps and a stochastic surplus production model. The results show that the most effective HCR includes both a biomass threshold as well as the P* method, and leads to high and stable long-term yield with a decreased risk of low stock biomass. For highly dynamics stocks, management strategies that aim for higher biomass targets than the traditionally used BMSY result in higher long-term yield. This study makes the case for probability-based HCRs by demonstrating their benefit over deterministic HCRs and provides a list of recommendations regarding their definition and implementation.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
Canadian Journal of Fisheries and Aquatic Sciences
18 papers in training set
Top 0.1%
31.7%
2
ICES Journal of Marine Science
11 papers in training set
Top 0.1%
13.1%
3
Peer Community Journal
281 papers in training set
Top 0.7%
6.4%
50% of probability mass above
4
Royal Society Open Science
214 papers in training set
Top 1%
3.3%
5
PLOS ONE
5266 papers in training set
Top 37%
3.3%
6
Ecological Modelling
28 papers in training set
Top 0.2%
3.3%
7
Ecology and Evolution
267 papers in training set
Top 2%
3.3%
8
Methods in Ecology and Evolution
176 papers in training set
Top 0.7%
2.8%
9
Population Ecology
10 papers in training set
Top 0.1%
2.7%
10
Ecological Informatics
33 papers in training set
Top 0.3%
2.4%
11
Frontiers in Marine Science
62 papers in training set
Top 0.5%
2.2%
12
Scientific Reports
3612 papers in training set
Top 50%
1.9%
13
Conservation Letters
14 papers in training set
Top 0.1%
1.8%
14
Aquatic Conservation: Marine and Freshwater Ecosystems
12 papers in training set
Top 0.2%
1.8%
15
PeerJ
308 papers in training set
Top 6%
1.5%
16
Science of The Total Environment
186 papers in training set
Top 2%
1.5%
17
Journal of Applied Ecology
39 papers in training set
Top 0.8%
1.1%
18
PLOS Computational Biology
1863 papers in training set
Top 17%
1.1%
19
Frontiers in Ecology and Evolution
69 papers in training set
Top 2%
1.1%
20
Ecosphere
57 papers in training set
Top 1%
1.1%
21
Biological Conservation
46 papers in training set
Top 0.8%
1.0%
22
Conservation Biology
17 papers in training set
Top 0.3%
0.9%
23
Nature Communications
5641 papers in training set
Top 56%
0.9%
24
Evolutionary Applications
108 papers in training set
Top 2%
0.6%